{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年</th>\n",
       "      <th>月</th>\n",
       "      <th>公司</th>\n",
       "      <th>营业收入</th>\n",
       "      <th>营业成本</th>\n",
       "      <th>利润总额</th>\n",
       "      <th>净 利 润</th>\n",
       "      <th>资产合计</th>\n",
       "      <th>负债合计</th>\n",
       "      <th>权益合计</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10104</td>\n",
       "      <td>34603.70</td>\n",
       "      <td>52479.27</td>\n",
       "      <td>6140.64</td>\n",
       "      <td>8573.19</td>\n",
       "      <td>355024.70</td>\n",
       "      <td>-584170.26</td>\n",
       "      <td>939194.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10105</td>\n",
       "      <td>158326.61</td>\n",
       "      <td>142732.97</td>\n",
       "      <td>14948.80</td>\n",
       "      <td>14211.79</td>\n",
       "      <td>715383.07</td>\n",
       "      <td>736184.44</td>\n",
       "      <td>-20801.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10108</td>\n",
       "      <td>98515.01</td>\n",
       "      <td>88478.95</td>\n",
       "      <td>8204.14</td>\n",
       "      <td>6658.71</td>\n",
       "      <td>545261.37</td>\n",
       "      <td>344963.70</td>\n",
       "      <td>200297.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10110</td>\n",
       "      <td>21531.00</td>\n",
       "      <td>20762.68</td>\n",
       "      <td>361.40</td>\n",
       "      <td>361.40</td>\n",
       "      <td>134095.92</td>\n",
       "      <td>294480.98</td>\n",
       "      <td>-160385.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10111</td>\n",
       "      <td>27109.50</td>\n",
       "      <td>26018.61</td>\n",
       "      <td>578.37</td>\n",
       "      <td>578.37</td>\n",
       "      <td>158604.36</td>\n",
       "      <td>319261.84</td>\n",
       "      <td>-160657.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10112</td>\n",
       "      <td>29162.27</td>\n",
       "      <td>29653.10</td>\n",
       "      <td>-916.89</td>\n",
       "      <td>-916.89</td>\n",
       "      <td>194937.99</td>\n",
       "      <td>404036.88</td>\n",
       "      <td>-209098.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10114</td>\n",
       "      <td>43614.99</td>\n",
       "      <td>39925.33</td>\n",
       "      <td>4102.77</td>\n",
       "      <td>4102.77</td>\n",
       "      <td>226810.98</td>\n",
       "      <td>311832.09</td>\n",
       "      <td>-85021.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10115</td>\n",
       "      <td>18332.33</td>\n",
       "      <td>17477.32</td>\n",
       "      <td>235.95</td>\n",
       "      <td>235.95</td>\n",
       "      <td>179557.53</td>\n",
       "      <td>245760.34</td>\n",
       "      <td>-66202.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10116</td>\n",
       "      <td>87928.16</td>\n",
       "      <td>88074.99</td>\n",
       "      <td>-1786.72</td>\n",
       "      <td>2448.02</td>\n",
       "      <td>504243.63</td>\n",
       "      <td>805788.05</td>\n",
       "      <td>-301544.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10118</td>\n",
       "      <td>38146.61</td>\n",
       "      <td>35663.47</td>\n",
       "      <td>836.84</td>\n",
       "      <td>610.89</td>\n",
       "      <td>268202.71</td>\n",
       "      <td>242816.38</td>\n",
       "      <td>25386.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10119</td>\n",
       "      <td>36350.04</td>\n",
       "      <td>31487.55</td>\n",
       "      <td>3403.19</td>\n",
       "      <td>2484.33</td>\n",
       "      <td>229158.83</td>\n",
       "      <td>6774.04</td>\n",
       "      <td>222384.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10120</td>\n",
       "      <td>62620.31</td>\n",
       "      <td>56045.07</td>\n",
       "      <td>3580.47</td>\n",
       "      <td>5221.15</td>\n",
       "      <td>362651.05</td>\n",
       "      <td>388532.98</td>\n",
       "      <td>-25881.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10121</td>\n",
       "      <td>20016.11</td>\n",
       "      <td>17741.88</td>\n",
       "      <td>1987.44</td>\n",
       "      <td>1987.44</td>\n",
       "      <td>120663.58</td>\n",
       "      <td>127874.05</td>\n",
       "      <td>-7210.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10124</td>\n",
       "      <td>35840.56</td>\n",
       "      <td>32305.05</td>\n",
       "      <td>3340.37</td>\n",
       "      <td>3340.37</td>\n",
       "      <td>247591.22</td>\n",
       "      <td>240248.21</td>\n",
       "      <td>7343.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10125</td>\n",
       "      <td>17929.99</td>\n",
       "      <td>14860.52</td>\n",
       "      <td>5267.88</td>\n",
       "      <td>3845.55</td>\n",
       "      <td>107222.72</td>\n",
       "      <td>15874.91</td>\n",
       "      <td>91347.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10126</td>\n",
       "      <td>8919.85</td>\n",
       "      <td>7213.61</td>\n",
       "      <td>1543.41</td>\n",
       "      <td>1126.69</td>\n",
       "      <td>38491.98</td>\n",
       "      <td>-206471.87</td>\n",
       "      <td>244963.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10128</td>\n",
       "      <td>16120.88</td>\n",
       "      <td>13559.84</td>\n",
       "      <td>2195.15</td>\n",
       "      <td>1602.46</td>\n",
       "      <td>85179.97</td>\n",
       "      <td>-788.80</td>\n",
       "      <td>85968.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10129</td>\n",
       "      <td>12044.22</td>\n",
       "      <td>10156.35</td>\n",
       "      <td>1339.24</td>\n",
       "      <td>977.64</td>\n",
       "      <td>94156.96</td>\n",
       "      <td>-66331.93</td>\n",
       "      <td>160488.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10130</td>\n",
       "      <td>153563.41</td>\n",
       "      <td>135963.81</td>\n",
       "      <td>20668.97</td>\n",
       "      <td>15088.35</td>\n",
       "      <td>624139.34</td>\n",
       "      <td>208453.80</td>\n",
       "      <td>415685.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10132</td>\n",
       "      <td>299814.70</td>\n",
       "      <td>257310.25</td>\n",
       "      <td>36019.18</td>\n",
       "      <td>26294.00</td>\n",
       "      <td>1449960.11</td>\n",
       "      <td>290872.05</td>\n",
       "      <td>1159088.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10133</td>\n",
       "      <td>3524.27</td>\n",
       "      <td>2798.05</td>\n",
       "      <td>682.37</td>\n",
       "      <td>498.13</td>\n",
       "      <td>13292.78</td>\n",
       "      <td>-65357.70</td>\n",
       "      <td>78650.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>506.46</td>\n",
       "      <td>-81.56</td>\n",
       "      <td>-81.56</td>\n",
       "      <td>29220.41</td>\n",
       "      <td>13853.87</td>\n",
       "      <td>15366.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>824.12</td>\n",
       "      <td>-177.19</td>\n",
       "      <td>-177.19</td>\n",
       "      <td>1267.63</td>\n",
       "      <td>689.98</td>\n",
       "      <td>577.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1699.37</td>\n",
       "      <td>-373.81</td>\n",
       "      <td>-373.81</td>\n",
       "      <td>1008.69</td>\n",
       "      <td>916.18</td>\n",
       "      <td>92.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>765.63</td>\n",
       "      <td>-159.07</td>\n",
       "      <td>-159.07</td>\n",
       "      <td>90.17</td>\n",
       "      <td>-812.89</td>\n",
       "      <td>903.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10144</td>\n",
       "      <td>15909.72</td>\n",
       "      <td>15959.08</td>\n",
       "      <td>-341.39</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>142593.14</td>\n",
       "      <td>134642.58</td>\n",
       "      <td>7950.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>10104</td>\n",
       "      <td>40715.50</td>\n",
       "      <td>55229.33</td>\n",
       "      <td>17189.82</td>\n",
       "      <td>16639.09</td>\n",
       "      <td>387830.29</td>\n",
       "      <td>-568003.76</td>\n",
       "      <td>955834.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>10105</td>\n",
       "      <td>150221.34</td>\n",
       "      <td>139133.70</td>\n",
       "      <td>14053.34</td>\n",
       "      <td>13360.48</td>\n",
       "      <td>732872.47</td>\n",
       "      <td>740313.36</td>\n",
       "      <td>-7440.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>10108</td>\n",
       "      <td>115602.06</td>\n",
       "      <td>97121.47</td>\n",
       "      <td>16379.27</td>\n",
       "      <td>13293.88</td>\n",
       "      <td>558379.53</td>\n",
       "      <td>344787.99</td>\n",
       "      <td>213591.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sep</td>\n",
       "      <td>10142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>49.58</td>\n",
       "      <td>-49.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sep</td>\n",
       "      <td>10144</td>\n",
       "      <td>20207.47</td>\n",
       "      <td>19804.13</td>\n",
       "      <td>-48.79</td>\n",
       "      <td>-57.17</td>\n",
       "      <td>226968.82</td>\n",
       "      <td>196527.96</td>\n",
       "      <td>30440.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sep</td>\n",
       "      <td>10158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-938.69</td>\n",
       "      <td>939.92</td>\n",
       "      <td>939.92</td>\n",
       "      <td>6524.68</td>\n",
       "      <td>3923.99</td>\n",
       "      <td>2600.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10104</td>\n",
       "      <td>39016.85</td>\n",
       "      <td>64776.05</td>\n",
       "      <td>10325.21</td>\n",
       "      <td>7715.37</td>\n",
       "      <td>729137.89</td>\n",
       "      <td>-318167.67</td>\n",
       "      <td>1047305.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10105</td>\n",
       "      <td>182027.11</td>\n",
       "      <td>173684.51</td>\n",
       "      <td>2569.84</td>\n",
       "      <td>1463.32</td>\n",
       "      <td>1349212.11</td>\n",
       "      <td>1193326.60</td>\n",
       "      <td>155885.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10108</td>\n",
       "      <td>103944.49</td>\n",
       "      <td>100386.72</td>\n",
       "      <td>1330.66</td>\n",
       "      <td>742.64</td>\n",
       "      <td>931362.22</td>\n",
       "      <td>624018.29</td>\n",
       "      <td>307343.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10110</td>\n",
       "      <td>28432.39</td>\n",
       "      <td>28524.70</td>\n",
       "      <td>-923.58</td>\n",
       "      <td>-923.58</td>\n",
       "      <td>237915.33</td>\n",
       "      <td>378470.93</td>\n",
       "      <td>-140555.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10111</td>\n",
       "      <td>31185.72</td>\n",
       "      <td>32586.43</td>\n",
       "      <td>-2319.92</td>\n",
       "      <td>-2319.92</td>\n",
       "      <td>347653.23</td>\n",
       "      <td>519501.00</td>\n",
       "      <td>-171847.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10112</td>\n",
       "      <td>38821.15</td>\n",
       "      <td>42141.39</td>\n",
       "      <td>-3825.95</td>\n",
       "      <td>-3825.95</td>\n",
       "      <td>525135.07</td>\n",
       "      <td>771695.97</td>\n",
       "      <td>-246560.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10114</td>\n",
       "      <td>59952.39</td>\n",
       "      <td>59207.15</td>\n",
       "      <td>-1288.99</td>\n",
       "      <td>-1288.99</td>\n",
       "      <td>494708.57</td>\n",
       "      <td>531803.93</td>\n",
       "      <td>-37095.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10115</td>\n",
       "      <td>33018.10</td>\n",
       "      <td>35759.82</td>\n",
       "      <td>-4128.61</td>\n",
       "      <td>-4128.61</td>\n",
       "      <td>374106.62</td>\n",
       "      <td>459083.31</td>\n",
       "      <td>-84976.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10116</td>\n",
       "      <td>117756.42</td>\n",
       "      <td>116332.52</td>\n",
       "      <td>-980.06</td>\n",
       "      <td>-984.42</td>\n",
       "      <td>827948.87</td>\n",
       "      <td>1024896.36</td>\n",
       "      <td>-196947.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10118</td>\n",
       "      <td>48565.46</td>\n",
       "      <td>49753.48</td>\n",
       "      <td>-2527.05</td>\n",
       "      <td>-2131.78</td>\n",
       "      <td>511999.63</td>\n",
       "      <td>451216.03</td>\n",
       "      <td>60783.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10119</td>\n",
       "      <td>51278.62</td>\n",
       "      <td>46517.02</td>\n",
       "      <td>4159.52</td>\n",
       "      <td>3054.00</td>\n",
       "      <td>422634.52</td>\n",
       "      <td>115521.78</td>\n",
       "      <td>307112.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10120</td>\n",
       "      <td>75716.42</td>\n",
       "      <td>74871.32</td>\n",
       "      <td>-1871.85</td>\n",
       "      <td>-1551.88</td>\n",
       "      <td>641979.05</td>\n",
       "      <td>600805.55</td>\n",
       "      <td>41173.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10121</td>\n",
       "      <td>24774.18</td>\n",
       "      <td>24776.26</td>\n",
       "      <td>-730.02</td>\n",
       "      <td>-571.00</td>\n",
       "      <td>200827.13</td>\n",
       "      <td>170948.59</td>\n",
       "      <td>29878.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10124</td>\n",
       "      <td>47015.42</td>\n",
       "      <td>45801.16</td>\n",
       "      <td>-69.10</td>\n",
       "      <td>-122.44</td>\n",
       "      <td>430903.19</td>\n",
       "      <td>386819.07</td>\n",
       "      <td>44084.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10125</td>\n",
       "      <td>23392.01</td>\n",
       "      <td>20115.95</td>\n",
       "      <td>2379.88</td>\n",
       "      <td>1767.34</td>\n",
       "      <td>223340.79</td>\n",
       "      <td>81994.38</td>\n",
       "      <td>141346.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10126</td>\n",
       "      <td>8235.84</td>\n",
       "      <td>6634.70</td>\n",
       "      <td>1367.55</td>\n",
       "      <td>995.78</td>\n",
       "      <td>80853.99</td>\n",
       "      <td>-200468.76</td>\n",
       "      <td>281322.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10128</td>\n",
       "      <td>18397.44</td>\n",
       "      <td>17148.15</td>\n",
       "      <td>593.92</td>\n",
       "      <td>387.88</td>\n",
       "      <td>186318.05</td>\n",
       "      <td>56911.31</td>\n",
       "      <td>129406.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10129</td>\n",
       "      <td>16304.37</td>\n",
       "      <td>14802.47</td>\n",
       "      <td>654.87</td>\n",
       "      <td>433.03</td>\n",
       "      <td>226452.18</td>\n",
       "      <td>27956.55</td>\n",
       "      <td>198495.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10130</td>\n",
       "      <td>170366.36</td>\n",
       "      <td>163503.40</td>\n",
       "      <td>1499.31</td>\n",
       "      <td>524.81</td>\n",
       "      <td>1640046.77</td>\n",
       "      <td>999153.73</td>\n",
       "      <td>640893.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10132</td>\n",
       "      <td>332386.74</td>\n",
       "      <td>304510.14</td>\n",
       "      <td>23899.79</td>\n",
       "      <td>17001.87</td>\n",
       "      <td>2558114.76</td>\n",
       "      <td>885187.18</td>\n",
       "      <td>1672927.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10133</td>\n",
       "      <td>3334.98</td>\n",
       "      <td>2531.44</td>\n",
       "      <td>738.93</td>\n",
       "      <td>555.97</td>\n",
       "      <td>11032.97</td>\n",
       "      <td>-80476.68</td>\n",
       "      <td>91509.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1022.25</td>\n",
       "      <td>-451.98</td>\n",
       "      <td>-674.30</td>\n",
       "      <td>42525.16</td>\n",
       "      <td>19461.52</td>\n",
       "      <td>23063.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>330.37</td>\n",
       "      <td>-330.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>901.33</td>\n",
       "      <td>-901.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>49.58</td>\n",
       "      <td>-49.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10144</td>\n",
       "      <td>21908.15</td>\n",
       "      <td>21280.99</td>\n",
       "      <td>1.10</td>\n",
       "      <td>-11.36</td>\n",
       "      <td>228100.25</td>\n",
       "      <td>197670.76</td>\n",
       "      <td>30429.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1785.86</td>\n",
       "      <td>-1777.20</td>\n",
       "      <td>-1777.20</td>\n",
       "      <td>6208.17</td>\n",
       "      <td>5384.68</td>\n",
       "      <td>823.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>594 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        年    月     公司       营业收入       营业成本      利润总额     净 利 润        资产合计  \\\n",
       "0    2018  Jan  10104   34603.70   52479.27   6140.64   8573.19   355024.70   \n",
       "1    2018  Jan  10105  158326.61  142732.97  14948.80  14211.79   715383.07   \n",
       "2    2018  Jan  10108   98515.01   88478.95   8204.14   6658.71   545261.37   \n",
       "3    2018  Jan  10110   21531.00   20762.68    361.40    361.40   134095.92   \n",
       "4    2018  Jan  10111   27109.50   26018.61    578.37    578.37   158604.36   \n",
       "5    2018  Jan  10112   29162.27   29653.10   -916.89   -916.89   194937.99   \n",
       "6    2018  Jan  10114   43614.99   39925.33   4102.77   4102.77   226810.98   \n",
       "7    2018  Jan  10115   18332.33   17477.32    235.95    235.95   179557.53   \n",
       "8    2018  Jan  10116   87928.16   88074.99  -1786.72   2448.02   504243.63   \n",
       "9    2018  Jan  10118   38146.61   35663.47    836.84    610.89   268202.71   \n",
       "10   2018  Jan  10119   36350.04   31487.55   3403.19   2484.33   229158.83   \n",
       "11   2018  Jan  10120   62620.31   56045.07   3580.47   5221.15   362651.05   \n",
       "12   2018  Jan  10121   20016.11   17741.88   1987.44   1987.44   120663.58   \n",
       "13   2018  Jan  10124   35840.56   32305.05   3340.37   3340.37   247591.22   \n",
       "14   2018  Jan  10125   17929.99   14860.52   5267.88   3845.55   107222.72   \n",
       "15   2018  Jan  10126    8919.85    7213.61   1543.41   1126.69    38491.98   \n",
       "16   2018  Jan  10128   16120.88   13559.84   2195.15   1602.46    85179.97   \n",
       "17   2018  Jan  10129   12044.22   10156.35   1339.24    977.64    94156.96   \n",
       "18   2018  Jan  10130  153563.41  135963.81  20668.97  15088.35   624139.34   \n",
       "19   2018  Jan  10132  299814.70  257310.25  36019.18  26294.00  1449960.11   \n",
       "20   2018  Jan  10133    3524.27    2798.05    682.37    498.13    13292.78   \n",
       "21   2018  Jan  10138        NaN     506.46    -81.56    -81.56    29220.41   \n",
       "22   2018  Jan  10140        NaN     824.12   -177.19   -177.19     1267.63   \n",
       "23   2018  Jan  10141        NaN    1699.37   -373.81   -373.81     1008.69   \n",
       "24   2018  Jan  10142        NaN     765.63   -159.07   -159.07       90.17   \n",
       "25   2018  Jan  10144   15909.72   15959.08   -341.39     -2.81   142593.14   \n",
       "26   2018  Jan  10158        NaN        NaN       NaN       NaN         NaN   \n",
       "27   2018  Feb  10104   40715.50   55229.33  17189.82  16639.09   387830.29   \n",
       "28   2018  Feb  10105  150221.34  139133.70  14053.34  13360.48   732872.47   \n",
       "29   2018  Feb  10108  115602.06   97121.47  16379.27  13293.88   558379.53   \n",
       "..    ...  ...    ...        ...        ...       ...       ...         ...   \n",
       "564  2019  Sep  10142        NaN       0.00      0.00      0.00        0.00   \n",
       "565  2019  Sep  10144   20207.47   19804.13    -48.79    -57.17   226968.82   \n",
       "566  2019  Sep  10158        NaN    -938.69    939.92    939.92     6524.68   \n",
       "567  2019  Oct  10104   39016.85   64776.05  10325.21   7715.37   729137.89   \n",
       "568  2019  Oct  10105  182027.11  173684.51   2569.84   1463.32  1349212.11   \n",
       "569  2019  Oct  10108  103944.49  100386.72   1330.66    742.64   931362.22   \n",
       "570  2019  Oct  10110   28432.39   28524.70   -923.58   -923.58   237915.33   \n",
       "571  2019  Oct  10111   31185.72   32586.43  -2319.92  -2319.92   347653.23   \n",
       "572  2019  Oct  10112   38821.15   42141.39  -3825.95  -3825.95   525135.07   \n",
       "573  2019  Oct  10114   59952.39   59207.15  -1288.99  -1288.99   494708.57   \n",
       "574  2019  Oct  10115   33018.10   35759.82  -4128.61  -4128.61   374106.62   \n",
       "575  2019  Oct  10116  117756.42  116332.52   -980.06   -984.42   827948.87   \n",
       "576  2019  Oct  10118   48565.46   49753.48  -2527.05  -2131.78   511999.63   \n",
       "577  2019  Oct  10119   51278.62   46517.02   4159.52   3054.00   422634.52   \n",
       "578  2019  Oct  10120   75716.42   74871.32  -1871.85  -1551.88   641979.05   \n",
       "579  2019  Oct  10121   24774.18   24776.26   -730.02   -571.00   200827.13   \n",
       "580  2019  Oct  10124   47015.42   45801.16    -69.10   -122.44   430903.19   \n",
       "581  2019  Oct  10125   23392.01   20115.95   2379.88   1767.34   223340.79   \n",
       "582  2019  Oct  10126    8235.84    6634.70   1367.55    995.78    80853.99   \n",
       "583  2019  Oct  10128   18397.44   17148.15    593.92    387.88   186318.05   \n",
       "584  2019  Oct  10129   16304.37   14802.47    654.87    433.03   226452.18   \n",
       "585  2019  Oct  10130  170366.36  163503.40   1499.31    524.81  1640046.77   \n",
       "586  2019  Oct  10132  332386.74  304510.14  23899.79  17001.87  2558114.76   \n",
       "587  2019  Oct  10133    3334.98    2531.44    738.93    555.97    11032.97   \n",
       "588  2019  Oct  10138        NaN    1022.25   -451.98   -674.30    42525.16   \n",
       "589  2019  Oct  10140        NaN       0.00      0.00      0.00        0.00   \n",
       "590  2019  Oct  10141        NaN      -0.02      0.02      0.02        0.02   \n",
       "591  2019  Oct  10142        NaN       0.00      0.00      0.00        0.00   \n",
       "592  2019  Oct  10144   21908.15   21280.99      1.10    -11.36   228100.25   \n",
       "593  2019  Oct  10158        NaN    1785.86  -1777.20  -1777.20     6208.17   \n",
       "\n",
       "           负债合计        权益合计  \n",
       "0    -584170.26   939194.95  \n",
       "1     736184.44   -20801.37  \n",
       "2     344963.70   200297.66  \n",
       "3     294480.98  -160385.06  \n",
       "4     319261.84  -160657.48  \n",
       "5     404036.88  -209098.89  \n",
       "6     311832.09   -85021.11  \n",
       "7     245760.34   -66202.81  \n",
       "8     805788.05  -301544.42  \n",
       "9     242816.38    25386.33  \n",
       "10      6774.04   222384.79  \n",
       "11    388532.98   -25881.93  \n",
       "12    127874.05    -7210.47  \n",
       "13    240248.21     7343.01  \n",
       "14     15874.91    91347.81  \n",
       "15   -206471.87   244963.85  \n",
       "16      -788.80    85968.77  \n",
       "17    -66331.93   160488.89  \n",
       "18    208453.80   415685.54  \n",
       "19    290872.05  1159088.06  \n",
       "20    -65357.70    78650.48  \n",
       "21     13853.87    15366.55  \n",
       "22       689.98      577.66  \n",
       "23       916.18       92.51  \n",
       "24      -812.89      903.06  \n",
       "25    134642.58     7950.55  \n",
       "26          NaN         NaN  \n",
       "27   -568003.76   955834.05  \n",
       "28    740313.36    -7440.89  \n",
       "29    344787.99   213591.54  \n",
       "..          ...         ...  \n",
       "564       49.58      -49.58  \n",
       "565   196527.96    30440.86  \n",
       "566     3923.99     2600.68  \n",
       "567  -318167.67  1047305.56  \n",
       "568  1193326.60   155885.51  \n",
       "569   624018.29   307343.93  \n",
       "570   378470.93  -140555.60  \n",
       "571   519501.00  -171847.77  \n",
       "572   771695.97  -246560.90  \n",
       "573   531803.93   -37095.35  \n",
       "574   459083.31   -84976.68  \n",
       "575  1024896.36  -196947.49  \n",
       "576   451216.03    60783.59  \n",
       "577   115521.78   307112.74  \n",
       "578   600805.55    41173.50  \n",
       "579   170948.59    29878.54  \n",
       "580   386819.07    44084.12  \n",
       "581    81994.38   141346.41  \n",
       "582  -200468.76   281322.75  \n",
       "583    56911.31   129406.74  \n",
       "584    27956.55   198495.63  \n",
       "585   999153.73   640893.04  \n",
       "586   885187.18  1672927.58  \n",
       "587   -80476.68    91509.65  \n",
       "588    19461.52    23063.64  \n",
       "589      330.37     -330.37  \n",
       "590      901.33     -901.31  \n",
       "591       49.58      -49.58  \n",
       "592   197670.76    30429.50  \n",
       "593     5384.68      823.48  \n",
       "\n",
       "[594 rows x 10 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_excel('d:/jupyter_code/data/fin_data.xlsx',sheet_name=0,converters={'年':str,'公司':str})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openpyxl\n",
    "from openpyxl.utils.dataframe import dataframe_to_rows\n",
    "wb = openpyxl.Workbook()\n",
    "def writesheet(group):\n",
    "    data = group.reset_index(drop=True)\n",
    "    ws = wb.create_sheet(title=data.loc[0,'公司'])\n",
    "    for r in dataframe_to_rows(data,index=False,header=True):\n",
    "        ws.append(r)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.groupby('公司').apply(writesheet)\n",
    "wb.save('案例1结果.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def writesheet(group):\n",
    "    data = group.reset_index(drop=True)\n",
    "    data.to_excel(r'案例1结果/'+data.loc[0,'公司']+'.xlsx',index=False)\n",
    "    #print(data.loc[0,'公司'])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('公司',as_index=False).apply(writesheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(4,3))\n",
    "plt.subplot(1,1,1)\n",
    "x=[0,1,2,3,4]\n",
    "y=[3,1,4,5,2]\n",
    "plt.plot(x,y)\n",
    "plt.ylabel('y轴标签')\n",
    "plt.title('标题：Matplotlib作图案例')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.family']='SimHei'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthdict={'Jan':'01','Feb':'02','Mar':'03','Apr':'04','May':'05','Jun':'06','Jul':'07','Aug':'08','Sep':'09','Oct':'10','Nov':'11','Dec':'12'}\n",
    "df1.insert(2,'月2',value=df1['月'].map(lambda x:monthdict[x]))\n",
    "df2=df1.sort_values(by=['公司','年','月2'],ascending=True).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年</th>\n",
       "      <th>月</th>\n",
       "      <th>月2</th>\n",
       "      <th>公司</th>\n",
       "      <th>营业收入</th>\n",
       "      <th>营业成本</th>\n",
       "      <th>利润总额</th>\n",
       "      <th>净 利 润</th>\n",
       "      <th>资产合计</th>\n",
       "      <th>负债合计</th>\n",
       "      <th>权益合计</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>01</td>\n",
       "      <td>10104</td>\n",
       "      <td>34603.70</td>\n",
       "      <td>52479.27</td>\n",
       "      <td>6140.64</td>\n",
       "      <td>8573.19</td>\n",
       "      <td>355024.70</td>\n",
       "      <td>-584170.26</td>\n",
       "      <td>939194.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>02</td>\n",
       "      <td>10104</td>\n",
       "      <td>40715.50</td>\n",
       "      <td>55229.33</td>\n",
       "      <td>17189.82</td>\n",
       "      <td>16639.09</td>\n",
       "      <td>387830.29</td>\n",
       "      <td>-568003.76</td>\n",
       "      <td>955834.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>Mar</td>\n",
       "      <td>03</td>\n",
       "      <td>10104</td>\n",
       "      <td>34226.96</td>\n",
       "      <td>47663.64</td>\n",
       "      <td>11791.60</td>\n",
       "      <td>19276.07</td>\n",
       "      <td>370153.14</td>\n",
       "      <td>-604956.98</td>\n",
       "      <td>975110.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>Apr</td>\n",
       "      <td>04</td>\n",
       "      <td>10104</td>\n",
       "      <td>34358.28</td>\n",
       "      <td>34938.26</td>\n",
       "      <td>76676.91</td>\n",
       "      <td>82385.76</td>\n",
       "      <td>643008.09</td>\n",
       "      <td>-414487.78</td>\n",
       "      <td>1057495.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>May</td>\n",
       "      <td>05</td>\n",
       "      <td>10104</td>\n",
       "      <td>35868.08</td>\n",
       "      <td>57795.18</td>\n",
       "      <td>6914.40</td>\n",
       "      <td>4982.51</td>\n",
       "      <td>434321.53</td>\n",
       "      <td>-628156.85</td>\n",
       "      <td>1062478.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      年    月  月2     公司      营业收入      营业成本      利润总额     净 利 润       资产合计  \\\n",
       "0  2018  Jan  01  10104  34603.70  52479.27   6140.64   8573.19  355024.70   \n",
       "1  2018  Feb  02  10104  40715.50  55229.33  17189.82  16639.09  387830.29   \n",
       "2  2018  Mar  03  10104  34226.96  47663.64  11791.60  19276.07  370153.14   \n",
       "3  2018  Apr  04  10104  34358.28  34938.26  76676.91  82385.76  643008.09   \n",
       "4  2018  May  05  10104  35868.08  57795.18   6914.40   4982.51  434321.53   \n",
       "\n",
       "        负债合计        权益合计  \n",
       "0 -584170.26   939194.95  \n",
       "1 -568003.76   955834.05  \n",
       "2 -604956.98   975110.12  \n",
       "3 -414487.78  1057495.87  \n",
       "4 -628156.85  1062478.38  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年</th>\n",
       "      <th>月</th>\n",
       "      <th>公司</th>\n",
       "      <th>销售量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10104</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10105</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10108</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10110</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10111</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10112</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10114</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10115</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10116</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10118</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10119</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10120</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10121</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10124</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10125</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10126</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10128</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10129</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10130</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10132</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10133</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10138</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10141</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10142</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10144</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>10104</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>10105</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>10108</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sep</td>\n",
       "      <td>10142</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sep</td>\n",
       "      <td>10144</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>2019</td>\n",
       "      <td>Sep</td>\n",
       "      <td>10158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10104</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10105</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10108</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10110</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10111</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10112</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10114</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10115</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10116</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10118</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10119</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10120</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10121</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10124</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10125</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10126</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10128</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10129</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10130</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10132</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10133</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10138</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10141</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10142</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10144</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>2019</td>\n",
       "      <td>Oct</td>\n",
       "      <td>10158</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>594 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        年    月     公司  销售量\n",
       "0    2018  Jan  10104   44\n",
       "1    2018  Jan  10105  168\n",
       "2    2018  Jan  10108  110\n",
       "3    2018  Jan  10110   12\n",
       "4    2018  Jan  10111   31\n",
       "5    2018  Jan  10112   37\n",
       "6    2018  Jan  10114   38\n",
       "7    2018  Jan  10115   22\n",
       "8    2018  Jan  10116   88\n",
       "9    2018  Jan  10118   32\n",
       "10   2018  Jan  10119   37\n",
       "11   2018  Jan  10120   54\n",
       "12   2018  Jan  10121   10\n",
       "13   2018  Jan  10124   45\n",
       "14   2018  Jan  10125   11\n",
       "15   2018  Jan  10126    0\n",
       "16   2018  Jan  10128   16\n",
       "17   2018  Jan  10129   12\n",
       "18   2018  Jan  10130  163\n",
       "19   2018  Jan  10132  312\n",
       "20   2018  Jan  10133    0\n",
       "21   2018  Jan  10138    0\n",
       "22   2018  Jan  10140    0\n",
       "23   2018  Jan  10141    0\n",
       "24   2018  Jan  10142    0\n",
       "25   2018  Jan  10144   21\n",
       "26   2018  Jan  10158    0\n",
       "27   2018  Feb  10104   42\n",
       "28   2018  Feb  10105  169\n",
       "29   2018  Feb  10108  141\n",
       "..    ...  ...    ...  ...\n",
       "564  2019  Sep  10142    0\n",
       "565  2019  Sep  10144   32\n",
       "566  2019  Sep  10158    0\n",
       "567  2019  Oct  10104   34\n",
       "568  2019  Oct  10105  170\n",
       "569  2019  Oct  10108   96\n",
       "570  2019  Oct  10110   29\n",
       "571  2019  Oct  10111   26\n",
       "572  2019  Oct  10112   44\n",
       "573  2019  Oct  10114   56\n",
       "574  2019  Oct  10115   20\n",
       "575  2019  Oct  10116  109\n",
       "576  2019  Oct  10118   46\n",
       "577  2019  Oct  10119   55\n",
       "578  2019  Oct  10120   69\n",
       "579  2019  Oct  10121   20\n",
       "580  2019  Oct  10124   43\n",
       "581  2019  Oct  10125   14\n",
       "582  2019  Oct  10126    0\n",
       "583  2019  Oct  10128    7\n",
       "584  2019  Oct  10129   12\n",
       "585  2019  Oct  10130  146\n",
       "586  2019  Oct  10132  298\n",
       "587  2019  Oct  10133    8\n",
       "588  2019  Oct  10138    0\n",
       "589  2019  Oct  10140    0\n",
       "590  2019  Oct  10141    0\n",
       "591  2019  Oct  10142    0\n",
       "592  2019  Oct  10144   14\n",
       "593  2019  Oct  10158    0\n",
       "\n",
       "[594 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.read_excel('d:/jupyter_code/data/fin_data.xlsx',sheet_name=1,converters={'年':str,'公司':str})\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年</th>\n",
       "      <th>月</th>\n",
       "      <th>月2</th>\n",
       "      <th>公司</th>\n",
       "      <th>营业收入</th>\n",
       "      <th>营业成本</th>\n",
       "      <th>利润总额</th>\n",
       "      <th>净 利 润</th>\n",
       "      <th>资产合计</th>\n",
       "      <th>负债合计</th>\n",
       "      <th>权益合计</th>\n",
       "      <th>销售量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>01</td>\n",
       "      <td>10104</td>\n",
       "      <td>34603.70</td>\n",
       "      <td>52479.27</td>\n",
       "      <td>6140.64</td>\n",
       "      <td>8573.19</td>\n",
       "      <td>355024.70</td>\n",
       "      <td>-584170.26</td>\n",
       "      <td>939194.95</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>02</td>\n",
       "      <td>10104</td>\n",
       "      <td>40715.50</td>\n",
       "      <td>55229.33</td>\n",
       "      <td>17189.82</td>\n",
       "      <td>16639.09</td>\n",
       "      <td>387830.29</td>\n",
       "      <td>-568003.76</td>\n",
       "      <td>955834.05</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>Mar</td>\n",
       "      <td>03</td>\n",
       "      <td>10104</td>\n",
       "      <td>34226.96</td>\n",
       "      <td>47663.64</td>\n",
       "      <td>11791.60</td>\n",
       "      <td>19276.07</td>\n",
       "      <td>370153.14</td>\n",
       "      <td>-604956.98</td>\n",
       "      <td>975110.12</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>Apr</td>\n",
       "      <td>04</td>\n",
       "      <td>10104</td>\n",
       "      <td>34358.28</td>\n",
       "      <td>34938.26</td>\n",
       "      <td>76676.91</td>\n",
       "      <td>82385.76</td>\n",
       "      <td>643008.09</td>\n",
       "      <td>-414487.78</td>\n",
       "      <td>1057495.87</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>May</td>\n",
       "      <td>05</td>\n",
       "      <td>10104</td>\n",
       "      <td>35868.08</td>\n",
       "      <td>57795.18</td>\n",
       "      <td>6914.40</td>\n",
       "      <td>4982.51</td>\n",
       "      <td>434321.53</td>\n",
       "      <td>-628156.85</td>\n",
       "      <td>1062478.38</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      年    月  月2     公司      营业收入      营业成本      利润总额     净 利 润       资产合计  \\\n",
       "0  2018  Jan  01  10104  34603.70  52479.27   6140.64   8573.19  355024.70   \n",
       "1  2018  Feb  02  10104  40715.50  55229.33  17189.82  16639.09  387830.29   \n",
       "2  2018  Mar  03  10104  34226.96  47663.64  11791.60  19276.07  370153.14   \n",
       "3  2018  Apr  04  10104  34358.28  34938.26  76676.91  82385.76  643008.09   \n",
       "4  2018  May  05  10104  35868.08  57795.18   6914.40   4982.51  434321.53   \n",
       "\n",
       "        负债合计        权益合计  销售量  \n",
       "0 -584170.26   939194.95   44  \n",
       "1 -568003.76   955834.05   42  \n",
       "2 -604956.98   975110.12   39  \n",
       "3 -414487.78  1057495.87   63  \n",
       "4 -628156.85  1062478.38   71  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.merge(df2,df3,how='left')\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年</th>\n",
       "      <th>月2</th>\n",
       "      <th>月</th>\n",
       "      <th>营业收入</th>\n",
       "      <th>营业成本</th>\n",
       "      <th>利润总额</th>\n",
       "      <th>净 利 润</th>\n",
       "      <th>资产合计</th>\n",
       "      <th>负债合计</th>\n",
       "      <th>权益合计</th>\n",
       "      <th>销售量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>01</td>\n",
       "      <td>Jan</td>\n",
       "      <td>1239924.24</td>\n",
       "      <td>1140463.33</td>\n",
       "      <td>111599.95</td>\n",
       "      <td>98535.87</td>\n",
       "      <td>6828810.84</td>\n",
       "      <td>4209923.90</td>\n",
       "      <td>2618886.93</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>02</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1421489.05</td>\n",
       "      <td>1234463.26</td>\n",
       "      <td>217407.65</td>\n",
       "      <td>187949.79</td>\n",
       "      <td>7155320.47</td>\n",
       "      <td>4348483.78</td>\n",
       "      <td>2806836.70</td>\n",
       "      <td>1645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>03</td>\n",
       "      <td>Mar</td>\n",
       "      <td>1251168.17</td>\n",
       "      <td>1138344.55</td>\n",
       "      <td>115955.70</td>\n",
       "      <td>104032.69</td>\n",
       "      <td>6996221.90</td>\n",
       "      <td>4084552.51</td>\n",
       "      <td>2911669.42</td>\n",
       "      <td>1793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>04</td>\n",
       "      <td>Apr</td>\n",
       "      <td>1232499.31</td>\n",
       "      <td>1152133.04</td>\n",
       "      <td>138009.39</td>\n",
       "      <td>119208.34</td>\n",
       "      <td>7077825.84</td>\n",
       "      <td>4046948.11</td>\n",
       "      <td>3030877.74</td>\n",
       "      <td>2211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>05</td>\n",
       "      <td>May</td>\n",
       "      <td>1274746.55</td>\n",
       "      <td>1191161.54</td>\n",
       "      <td>88094.57</td>\n",
       "      <td>83018.68</td>\n",
       "      <td>7066932.96</td>\n",
       "      <td>3953036.53</td>\n",
       "      <td>3113896.41</td>\n",
       "      <td>2264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>06</td>\n",
       "      <td>Jun</td>\n",
       "      <td>1350744.35</td>\n",
       "      <td>1223445.56</td>\n",
       "      <td>137012.01</td>\n",
       "      <td>113909.15</td>\n",
       "      <td>7163938.31</td>\n",
       "      <td>3936132.69</td>\n",
       "      <td>3227805.58</td>\n",
       "      <td>2027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>07</td>\n",
       "      <td>Jul</td>\n",
       "      <td>1519952.08</td>\n",
       "      <td>1345157.13</td>\n",
       "      <td>183910.72</td>\n",
       "      <td>162574.64</td>\n",
       "      <td>7426881.16</td>\n",
       "      <td>4036500.97</td>\n",
       "      <td>3390380.19</td>\n",
       "      <td>1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>08</td>\n",
       "      <td>Aug</td>\n",
       "      <td>1596895.87</td>\n",
       "      <td>1431211.73</td>\n",
       "      <td>169495.10</td>\n",
       "      <td>149785.04</td>\n",
       "      <td>7498648.18</td>\n",
       "      <td>3958482.95</td>\n",
       "      <td>3540165.21</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>09</td>\n",
       "      <td>Sep</td>\n",
       "      <td>1223536.81</td>\n",
       "      <td>1206392.99</td>\n",
       "      <td>10539.03</td>\n",
       "      <td>9843.94</td>\n",
       "      <td>7344734.80</td>\n",
       "      <td>3794725.59</td>\n",
       "      <td>3550009.17</td>\n",
       "      <td>1114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>Oct</td>\n",
       "      <td>1284852.26</td>\n",
       "      <td>1252555.28</td>\n",
       "      <td>26269.86</td>\n",
       "      <td>23145.62</td>\n",
       "      <td>7228520.71</td>\n",
       "      <td>3655365.93</td>\n",
       "      <td>3573154.82</td>\n",
       "      <td>1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>Nov</td>\n",
       "      <td>1169817.88</td>\n",
       "      <td>1138965.56</td>\n",
       "      <td>36382.47</td>\n",
       "      <td>32082.75</td>\n",
       "      <td>7124911.09</td>\n",
       "      <td>3519673.57</td>\n",
       "      <td>3605237.51</td>\n",
       "      <td>2776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>1294592.28</td>\n",
       "      <td>1259006.02</td>\n",
       "      <td>11788.34</td>\n",
       "      <td>15893.98</td>\n",
       "      <td>7093208.67</td>\n",
       "      <td>3552885.21</td>\n",
       "      <td>3540323.43</td>\n",
       "      <td>2047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       年  月2    月        营业收入        营业成本       利润总额      净 利 润        资产合计  \\\n",
       "0   2018  01  Jan  1239924.24  1140463.33  111599.95   98535.87  6828810.84   \n",
       "1   2018  02  Feb  1421489.05  1234463.26  217407.65  187949.79  7155320.47   \n",
       "2   2018  03  Mar  1251168.17  1138344.55  115955.70  104032.69  6996221.90   \n",
       "3   2018  04  Apr  1232499.31  1152133.04  138009.39  119208.34  7077825.84   \n",
       "4   2018  05  May  1274746.55  1191161.54   88094.57   83018.68  7066932.96   \n",
       "5   2018  06  Jun  1350744.35  1223445.56  137012.01  113909.15  7163938.31   \n",
       "6   2018  07  Jul  1519952.08  1345157.13  183910.72  162574.64  7426881.16   \n",
       "7   2018  08  Aug  1596895.87  1431211.73  169495.10  149785.04  7498648.18   \n",
       "8   2018  09  Sep  1223536.81  1206392.99   10539.03    9843.94  7344734.80   \n",
       "9   2018  10  Oct  1284852.26  1252555.28   26269.86   23145.62  7228520.71   \n",
       "10  2018  11  Nov  1169817.88  1138965.56   36382.47   32082.75  7124911.09   \n",
       "11  2018  12  Dec  1294592.28  1259006.02   11788.34   15893.98  7093208.67   \n",
       "\n",
       "          负债合计        权益合计   销售量  \n",
       "0   4209923.90  2618886.93  1263  \n",
       "1   4348483.78  2806836.70  1645  \n",
       "2   4084552.51  2911669.42  1793  \n",
       "3   4046948.11  3030877.74  2211  \n",
       "4   3953036.53  3113896.41  2264  \n",
       "5   3936132.69  3227805.58  2027  \n",
       "6   4036500.97  3390380.19  1947  \n",
       "7   3958482.95  3540165.21  1956  \n",
       "8   3794725.59  3550009.17  1114  \n",
       "9   3655365.93  3573154.82  1380  \n",
       "10  3519673.57  3605237.51  2776  \n",
       "11  3552885.21  3540323.43  2047  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df4.groupby(['年','月2','月'],as_index=False).sum()\n",
    "df6.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['年', '月2', '月', '营业收入', '营业成本', '利润总额', '净 利 润', '资产合计', '负债合计', '权益合计',\n",
       "       '销售量'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年</th>\n",
       "      <th>月</th>\n",
       "      <th>毛利率</th>\n",
       "      <th>利润率</th>\n",
       "      <th>净利率</th>\n",
       "      <th>资产净利率</th>\n",
       "      <th>权益净利率</th>\n",
       "      <th>平均单价</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jan</td>\n",
       "      <td>0.080215</td>\n",
       "      <td>0.090005</td>\n",
       "      <td>0.079469</td>\n",
       "      <td>0.014429</td>\n",
       "      <td>0.037625</td>\n",
       "      <td>981.729406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>0.131570</td>\n",
       "      <td>0.152944</td>\n",
       "      <td>0.132220</td>\n",
       "      <td>0.026267</td>\n",
       "      <td>0.066961</td>\n",
       "      <td>864.127082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>Mar</td>\n",
       "      <td>0.090175</td>\n",
       "      <td>0.092678</td>\n",
       "      <td>0.083148</td>\n",
       "      <td>0.014870</td>\n",
       "      <td>0.035730</td>\n",
       "      <td>697.807122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>Apr</td>\n",
       "      <td>0.065206</td>\n",
       "      <td>0.111975</td>\n",
       "      <td>0.096721</td>\n",
       "      <td>0.016843</td>\n",
       "      <td>0.039331</td>\n",
       "      <td>557.439760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>May</td>\n",
       "      <td>0.065570</td>\n",
       "      <td>0.069108</td>\n",
       "      <td>0.065126</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.026661</td>\n",
       "      <td>563.050596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jun</td>\n",
       "      <td>0.094243</td>\n",
       "      <td>0.101434</td>\n",
       "      <td>0.084331</td>\n",
       "      <td>0.015900</td>\n",
       "      <td>0.035290</td>\n",
       "      <td>666.376098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>Jul</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.120998</td>\n",
       "      <td>0.106960</td>\n",
       "      <td>0.021890</td>\n",
       "      <td>0.047952</td>\n",
       "      <td>780.663626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>Aug</td>\n",
       "      <td>0.103754</td>\n",
       "      <td>0.106140</td>\n",
       "      <td>0.093798</td>\n",
       "      <td>0.019975</td>\n",
       "      <td>0.042310</td>\n",
       "      <td>816.408931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>Sep</td>\n",
       "      <td>0.014012</td>\n",
       "      <td>0.008614</td>\n",
       "      <td>0.008045</td>\n",
       "      <td>0.001340</td>\n",
       "      <td>0.002773</td>\n",
       "      <td>1098.327478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>Oct</td>\n",
       "      <td>0.025137</td>\n",
       "      <td>0.020446</td>\n",
       "      <td>0.018014</td>\n",
       "      <td>0.003202</td>\n",
       "      <td>0.006478</td>\n",
       "      <td>931.052362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018</td>\n",
       "      <td>Nov</td>\n",
       "      <td>0.026374</td>\n",
       "      <td>0.031101</td>\n",
       "      <td>0.027425</td>\n",
       "      <td>0.004503</td>\n",
       "      <td>0.008899</td>\n",
       "      <td>421.404135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>Dec</td>\n",
       "      <td>0.027488</td>\n",
       "      <td>0.009106</td>\n",
       "      <td>0.012277</td>\n",
       "      <td>0.002241</td>\n",
       "      <td>0.004489</td>\n",
       "      <td>632.433942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       年    月       毛利率       利润率       净利率     资产净利率     权益净利率         平均单价\n",
       "0   2018  Jan  0.080215  0.090005  0.079469  0.014429  0.037625   981.729406\n",
       "1   2018  Feb  0.131570  0.152944  0.132220  0.026267  0.066961   864.127082\n",
       "2   2018  Mar  0.090175  0.092678  0.083148  0.014870  0.035730   697.807122\n",
       "3   2018  Apr  0.065206  0.111975  0.096721  0.016843  0.039331   557.439760\n",
       "4   2018  May  0.065570  0.069108  0.065126  0.011747  0.026661   563.050596\n",
       "5   2018  Jun  0.094243  0.101434  0.084331  0.015900  0.035290   666.376098\n",
       "6   2018  Jul  0.115000  0.120998  0.106960  0.021890  0.047952   780.663626\n",
       "7   2018  Aug  0.103754  0.106140  0.093798  0.019975  0.042310   816.408931\n",
       "8   2018  Sep  0.014012  0.008614  0.008045  0.001340  0.002773  1098.327478\n",
       "9   2018  Oct  0.025137  0.020446  0.018014  0.003202  0.006478   931.052362\n",
       "10  2018  Nov  0.026374  0.031101  0.027425  0.004503  0.008899   421.404135\n",
       "11  2018  Dec  0.027488  0.009106  0.012277  0.002241  0.004489   632.433942"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6['毛利率']=(df6['营业收入']-df6['营业成本'])/df6['营业收入']\n",
    "df6['利润率']=df6['利润总额']/df6['营业收入']\n",
    "df6['净利率']=df6['净 利 润']/df6['营业收入']\n",
    "df6['资产净利率']=df6['净 利 润']/df6['资产合计']\n",
    "df6['权益净利率']=df6['净 利 润']/df6['权益合计']\n",
    "df6['资产负载率']=df6['负债合计']/df6['资产合计']\n",
    "df6['平均单价']=df6['营业收入']/df6['销售量']\n",
    "df7=df6.loc[(df6['年']=='2018'),['年','月','毛利率','利润率','净利率','资产净利率','权益净利率','平均单价']]\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db7a70f320>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "#plt.figure(figsize=(12,4))\n",
    "#plt.subplot(1,1,1)\n",
    "plt.rcParams['font.family']='SimHei'    #设置中文字体显示\n",
    "plt.rcParams['axes.unicode_minus']=False   #设置中文状态负号-的正常显示 \n",
    "df7.plot(x='月',y=['毛利率','利润率','净利率','资产净利率','权益净利率'],title='2018年公司个指标利润统计',figsize=(12,4),rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db794fb128>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7.plot('月',['平均单价','毛利率'],secondary_y=['毛利率'],kind='bar', title='2018年平均单价&毛利率对比',figsize=(12,4),rot=0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,axes=plt.subplots(2,1,figsize=(12,8),sharex=True)\n",
    "ax0=df7.plot('月',['毛利率','利润率','净利率','资产净利率','权益净利率'],title='2018年公司各指标利率统计',ax=axes[0])\n",
    "ax1=df7.plot('月','平均单价',kind='bar',title='2018年平均单价&毛利率对比',color='gold',ax=axes[1])\n",
    "ax2=df7.plot('月','毛利率',secondary_y=True,color='orangered',ax=axes[1],style='--',marker='o',linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
